import cv2
import numpy as np
import msgpack
from datetime import datetime
from PIL import ImageFont, ImageDraw, Image


def process_frame_with_yolo(frame, channel_index, camera_configs, yolo_detector, redis_client):
    roi = camera_configs[channel_index]['box']
    types = camera_configs[channel_index]['types']
    height, width = frame.shape[:2]

    # ROI坐标转换
    x_min = int(roi[0] * width)
    y_min = int(roi[1] * height)
    x_max = int(roi[2] * width)
    y_max = int(roi[3] * height)
    roi_converted = (x_min, y_min, x_max - x_min, y_max - y_min)

    # 执行YOLO检测
    start_time = datetime.now()
    frame_with_boxes, results = yolo_detector.process_frame(frame, roi_converted)
    process_time = (datetime.now() - start_time).total_seconds() * 1000
    print(f"\n通道 {channel_index + 1} 检测结果 (处理时间: {process_time: .2f}ms):")

    detections = []
    person_boxes = []
    helmet_boxes = []
    safevest_boxes = []
    smoke_boxes = []
    other_detections = []

    if results is not None and hasattr(results, 'boxes') and len(results.boxes) > 0:
        # 新增中文映射
        class_mapping = {
            "animal": "异物入侵",
            "cellphone": "玩手机",
            "fire": "起火"
        }

        # 第一步：分类存储所有检测结果（根据types过滤）
        for box, conf, cls in zip(results.boxes.xyxy, results.boxes.conf, results.boxes.cls):
            class_name = results.names[int(cls)]
            if class_name not in types:  # 关键过滤逻辑
                continue

            x1, y1, x2, y2 = map(int, box)
            normalized_box = (
                x1 / width,
                y1 / height,
                (x2 - x1) / width,
                (y2 - y1) / height
            )

            if class_name == "person":
                person_boxes.append((box, conf, (x1, y1, x2, y2)))
            elif class_name == "helmet":
                helmet_boxes.append((x1, y1, x2, y2))
            elif class_name == "safevest":
                safevest_boxes.append((x1, y1, x2, y2))
            elif class_name == "smoke":
                smoke_boxes.append((x1, y1, x2, y2))
            elif class_name in ["animal", "cellphone", "fire"]:
                class_name_cn = class_mapping.get(class_name, class_name)
                other_detections.append({
                    "class": class_name_cn,
                    "confidence": float(conf),
                    "bbox": {
                        "x_min": x1 / width,
                        "y_min": y1 / height,
                        "width": (x2 - x1) / width,
                        "height": (y2 - y1) / height
                    }
                })
                print(f"- 独立检测: {class_name_cn}, 置信度: {conf: .2f}, 位置: {box.tolist()}")

        # 第二步：处理人员状态（仅在需要检测person时处理）
        detections = []
        if "person" in types:
            for (box, conf, (x1, y1, x2, y2)) in person_boxes:
                def calculate_iou(boxA, boxB):
                    xA = max(boxA[0], boxB[0])
                    yA = max(boxA[1], boxB[1])
                    xB = min(boxA[2], boxB[2])
                    yB = min(boxA[3], boxB[3])
                    interArea = max(0, xB - xA) * max(0, yB - yA)
                    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
                    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
                    return interArea / float(boxAArea + boxBArea - interArea)

                # 根据配置动态判断关联项
                has_helmet = any(calculate_iou((x1, y1, x2, y2), h_box) > 0.1 for h_box in helmet_boxes) if "helmet" in types else False
                has_safevest = any(calculate_iou((x1, y1, x2, y2), s_box) > 0.1 for s_box in safevest_boxes) if "safevest" in types else False
                has_smoke = any(calculate_iou((x1, y1, x2, y2), sm_box) > 0.1 for sm_box in smoke_boxes) if "smoke" in types else False

                # 生成状态标签
                status_label = "人员"
                violations = []
                if has_smoke:
                    status_label = "吸烟"
                else:
                    if "helmet" in types and not has_helmet:
                        violations.append("未戴安全帽")
                    if "safevest" in types and not has_safevest:
                        violations.append("未穿工服")
                    if violations:
                        status_label = "违规：" + "、".join(violations)
                    else:
                        if "helmet" in types or "safevest" in types:
                            status_label = "着装规范"

                detections.append({
                    "class": status_label,
                    "confidence": float(conf),
                    "bbox": {
                        "x_min": x1 / width,
                        "y_min": y1 / height,
                        "width": (x2 - x1) / width,
                        "height": (y2 - y1) / height
                    }
                })
                print(f"- 状态: {status_label}, 置信度: {conf:.2f}, 位置: {box}")

        # 添加独立检测类别
        detections.extend(other_detections)
        if not detections:
            print("- 未检测到任何目标")

    # 序列化并发送结果
    data = {
        "channel": str(camera_configs[channel_index]['channel']),
        "detections": detections,
        "image_size": [width, height]
    }

    try:
        serialized_data = msgpack.packb(data)
        redis_client.publish('detection_result_channel', serialized_data)
        print(f"[Redis] 通道 {camera_configs[channel_index]['channel']} 数据发送成功")
    except Exception as e:
        print(f"[Redis] 发送失败: {str(e)}")

    return frame_with_boxes